Identifiability of Causal Graphs using Functional Models

Understand

This work addresses the following question: Under what assumptions on the data generating process can one infer the causal graph from the joint distribution? The approach taken by conditional independence-based causal discovery methods is based on two assumptions: the Markov condition and faithfulness.

• It has been shown that under these assumptions the causal graph can be identified up to Markov equivalence (some arrows remain undirected) using methods like the PC algorithm.
• In this work we propose an alternative by defining Identifiable Functional Model Classes (IFMOCs).
• As our main theorem we prove that if the data generating process belongs to an IFMOC, one can identify the complete causal graph.